Errors in Modeling Carbon Turnover Induced by Temporal Temperature Aggregation

Modeling of C turnover is a common tool for the prediction of C stocks and CO2 efflux. It is well recognized that the choice of the input data (e. g., C pool sizes, hydraulic parameters, atmospheric boundary conditions) determines the outcome of these prediction. Temperature is known to be one of the most important driving factors and it varies in a range of temporal scales. Typically, the time discretization of most models is flexible and can range from minutes to months. However, the implications of variable time discretization for predicted soil C turnover are seldom discussed. In this study, we demonstrated that averaging of input temperature data will lead to changes in predicted C turnover in terms of daily amplitude and the impact of extreme temperatures. The results indicate that averaging from hourly to daily or monthly temperatures will lead to relative errors >4% yr(-1) for cumulative CO2 efflux. Instantaneous CO2 fluxes are even more affected, where daily and monthly averaging will lead to estimation errors exceeding 20%. We also show that a constant or daily variable temperature amplitude for rescaling daily average temperature did not decrease the error when using daily or monthly mean temperature instead of hourly data. Therefore, instantaneous fluxes are only accurately predicted when hourly temperature input is used. For long-term modeling (e.g., years to centuries), the relative error in cumulative efflux, and therefore in C stocks loss, is reasonably low (similar to 4-5% annual error) but will accumulate with time again.